The disclosed subject matter relates generally to manufacturing and, more particularly, to a method and apparatus for extracting data from a data store.
The manufacture of semiconductor devices requires a number of discrete process steps to create a packaged semiconductor circuit device from raw semiconductor material. The various processes, from the initial melt and refinement of the semiconductor material, the slicing of the semiconductor crystal into individual wafers, the fabrication stages (etching, doping, ion implanting or the like), to the packaging and final testing of the completed device, are so different from one another and specialized that the processes may be performed in different facilities in remote regions of the globe.
For example, the process of growing and refining a large semiconductor crystal (e.g., Si, GaAs, or the like) may be performed by a facility specializing in such crystal growth techniques. The resultant crystals may then be sold directly to a semiconductor manufacturer, either as large crystals, or as wafers, sliced from a large crystal. The semiconductor manufacturer may then slice the semiconductor crystal into wafers, if the semiconductor material is not already in wafer format. The semiconductor manufacturer then fabricates semiconductor circuit devices (e.g., microprocessor, DRAM, ASIC or the like) on individual wafers, usually forming a number of devices on each wafer.
Generally, a set of processing steps is performed on a wafer using a variety of processing tools, including photolithography steppers, etch tools, deposition tools, polishing tools, rapid thermal processing tools, implantation tools, etc. During the fabrication process various events may take place that affect the performance of the devices being fabricated. That is, variations in the fabrication process steps result in device performance variations. Factors, such as feature critical dimensions, doping levels, contact resistance, particle contamination, etc., all may potentially affect the end performance of the device. During the fabrication flow, various metrology parameters are collected for verifying the proper formation of the features on the wafer and/or to control the process tools to reduce variation and increase device performance and reliability.
After fabrication of the devices is complete, each wafer is subjected to preliminary functional tests, commonly referred to as final wafer electrical tests (FWET) that evaluate test structures on the wafer and SORT tests that evaluate each die. Wafers that pass these tests are then cut to singulate the individual die, which are then packed in substrates. Packed die are then subjected to additional tests against the specification of customers' orders to determine performance characteristics such as maximum operating speed, power, caches, etc. This packaging process is fairly labor intensive, and thus it may be desirable to perform the mounting, wire-bonding, and final testing at a remote facility. Once completed, the packaged semiconductor device may again be tested, and then labeled and shipped to customers through a distribution system.
One problem which arises in this prior art manufacturing technique, is that the various processes take place at different discrete locations. Thus, it is difficult to track a semiconductor device through the fabrication process, from single crystal to finished product. Such tracking may be necessary for quality control purposes in order to determine the causes of production problems which may result in low yields or circuit defects.
In present fabrication facilities, individual fabrication machines or computer aided manufacturing systems (CAM systems) may provide data regarding operating conditions during the fabrication process. Some of these data are intrinsic data, for example, lot numbers, device model numbers or the like. Other data may be extrinsic data, such as production test data, production conditions, or the like.
The large amount of data collected during manufacturing process requires the use of enterprise wide data collection and storage resources. Typically, such engineering databases store vast quantities of data. The vast quantity data gives rise to various data management issues. Often, a process engineer may want to gather data to evaluate a particular lot or process. However, the particular path a lot or group of lots traverses through the production flow it is not readily discernible. Hence, when a query is specified for extracting the data, wildcards are often used. The use of wildcards in data queries increases flexibility, but also greatly reduces the time required to gather data, as the wildcards need to be resolved by the database system. In addition, due to the number of different facilities involved in the fabrication process, data may be stored in different data warehouses that need to be linked to determine first if any data exists that satisfies the query, and second to extract the data. Given these conditions, data queries may consume significant processing resources and may also take a significant amount of time to complete.
This section of this document is intended to introduce various aspects of art that may be related to various aspects of the disclosed subject matter described and/or claimed below. This section provides background information to facilitate a better understanding of the various aspects of the disclosed subject matter. It should be understood that the statements in this section of this document are to be read in this light, and not as admissions of prior art. The disclosed subject matter is directed to overcoming, or at least reducing the effects of, one or more of the problems set forth above.